Bibliographic record
Abstract
[Objective]To understand the contents and the way of consumers' claims,the key points and focus which mostly concerned by the consumers,to provide the basis for the administration of Public Health Department to make decisions,so that some effective supervision methods could be adopted to safeguard consumer's health and the legitimate rights and interests.[Methods]241 health claims treated in Chaoyang Institute for Health Inspection and supervision during 2003-2005 were analyzed.[Results]From 2003 to 2005,241 health claims had been reported to the Institute,of them 85 cases in 2003,74 cases in 2004,and 82 cases in 2005;156 cases of them were through phone call,48 cases of them were letters transferred from higher authority.9 cases of them were letters directly from consumers and 28 cases of them were from visiting consumers;140 cases of them were related to food hygiene,78 cases related to medical market,11 cases related to infectious diseases,12 cases related to environmental hygiene in public places.Of the 241 cases,118 cases were made administrative sanctions;57 cases were ordered to stop professional activities and be fined;19 cases were ordered to stop business and illegally obtained were confiscated,at the same time be fined;17 cases were ordered to stop profession,and confiscating drugs and the instruments,at the same time be fined;25 cases were fined only.[Conclusion]In the 3rd quarter of every year,the claims related to food hygiene and medical market were the main cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".